where QH is the liver blood flow, fu b is the fraction unbound in the blood, and CLjnt (the intrinsic clearance) is a measure of the maximal ability of the liver to metabolize a drug in the absence of protein binding or blood flow limitations.

For drugs that are rapidly metabolized (i.e., very high CLint leading tofu,b • CLint»QH) the value of CL approaches QH and hepatic clearance is limited by the liver blood flow. For these compounds, interspecies differences in clearances will reflect interspecies differences in liver blood flow. One such example is propranolol. This drug is mainly eliminated by the liver, and the intrinsic clearance is so rapid that CL is limited by hepatic blood flow. Its CL is 90 mLmin _ 1 kg _ 1 in the rat, 34mLmin_ 1 kg_ 1 in the dog, 18 mLmin_ 1 kg_ 1 in the monkey, and 15 mLmin_ 1 kg_ 1 in man.52 The CL value in each species approximates their hepatic blood flow rate.

For drugs with low extraction ratios (/u b • CLint«QH) the value of CL is directly related to fu b and CLint (CL = fu b • CLint). Thus, the clearance of such compounds is mainly determined by their plasma protein binding and intrinsic clearance , which can both be subject to large species differences. Species differences in rate of metabolism (CLint) have been shown with many different drugs undergoing both phase I and phase II metabolism. In general, drug metabolism in mammalian species is lower than in nonmammalian species53 and decreases with increasing body weight. These species differences are the result of differences in the amino acid sequence, substrate specificity, and levels of the enzymes (e.g., cytochrome P450). For example, the hepatic levels of CYP1A, CYP2C and CYP3A in rats are approximately 28, 638, and 165pmolmg_ 1 microsomal protein, respectively,54 and the corresponding values for humans are 37, 55, and 87pmolmg_ 1 microsomal protein.55

In general, deficiencies are well known for some animal species often used in experimental studies. Such deficiencies are for example a lack of acetylation of some compounds in dogs and guinea pigs, glucuronidation deficiency in cats, and some deficiencies in sulfotransferase activities in pigs.16 In addition to these species differences in phase II conjugation reactions, differences are well known in phase I cytochrome P450 isoenzyme activities. However, for this group of enzymes it is more the altered substrate specificities and quantitative differences that lead to differences in metabolite patterns. The relevance of animal species depends on the cytochrome P450 isoenzyme involved. While dog seems to be a good species for CYP 2D-dependent reactions, CYP2E1 and CYP1A1/2 appear more appropriately represented in rats, (mini)pigs, and rhesus monkey.56 In addition to species differences significant gender differences are apparent, especially in rats. In general, male rats metabolize many drugs faster than female rats, while the opposite is reported for mice. In rats many of these differences are due to the male-specific cytochrome 2C11,57 which is often involved in the metabolism of compounds in man via CYP3A . New, humanized mouse models might overcome some of these limitations.13 Mice expressing human CYP2D6 and CYP3A4 have been successfully generated; however, many other components of human drug metabolism and transport might be important for consideration in such a holistic model to adequately represent the interplay of drug metabolism in man.

Large species differences in hepatic metabolic clearance have been reported for many drugs and have made extrapolation of metabolic clearance from animal to man highly questionable. Bosentan is one example of a drug characterized by large interspecies differences in clearance.58 Bosentan, exhibits a high blood clearance in rabbits, which approximates the liver blood flow. Intermediate to high clearance values were observed in mice, marmosets, and rats, while dogs exhibited a low blood clearance, which represented less than 5% of the corresponding liver blood flow. From these examples, it appears that it is not recommended to predict human metabolic clearance based on animal data. Fortunately, the availability of in vitro models in humans allows predictions of clearances to be performed based on physiologically-based scaling methods with satisfactory prediction accuracy.59'60

Comprehensive expert systems to predict drug metabolism are discussed in 5.33 Comprehensive Expert Systems to Predict Drug Metabolism. Interspecies differences in renal clearance

Many drugs and drug metabolites are excreted by the kidneys. Renal clearance is the net result of three interrelated processes: glomerular filtration, tubular secretion, and tubular reabsorption. Both glomerular filtration and active tubular excretion eliminate a drug from the blood, while tubular reabsorption allows drug that has entered the renal tubules to re-enter the circulation.

A mechanistic relationship, in which the contributions of tubular secretion and glomerular filtration are additive, has been proposed by Levy.61 This describes renal clearance (CLR) in the following terms:

CLr = (1 -/r)./u ■ GFR +(1 -f )■ QR[fu ■ CLmt,us]/[Qr + fu ■ CLmt,us] [5]

where CLR represents renal clearance, fr is the fraction of drug reabsorbed in the tubules, fu is the free fraction in plasma, GFR the glomerular filtration rate, QR the renal blood flow, and CLint,us is the intrinsic tubular secretion rate. This model assumes that tubular secretion and glomerular filtration are fully independent processes.

If tubular secretion is absent then CLR is directly proportional to GFR and the free fraction in plasma. For compounds with these characteristics, the unbound renal clearance in different species will reflect species differences in GFR. Similarly, if the secretory pathway is very efficient at removing drug then CLR approximates QR, and in this case species differences in the CLR will reflect species differences in renal blood flow. pra-Aminohippuric acid is one example of a compound whose renal clearance is limited by blood flow.

Lin62,63 recently provided a number of examples where mechanisms of renal excretion were compared across species. Based on these examples, depending on whether renal clearance is less than, equal to, or greater than the GFR, it may be possible to predict the mechanisms for renal elimination in man. Thus, famotidine has a renal clearance in rats of 1.68Lh-1 kg- 1 which exceeds its GFR (0.522Lh- 1kg- 1), indicating that a net tubular secretion is occurring. In humans, the renal clearance of famotidine is 0.266 Lh -1 kg -1, which is also greater its GFR (0.108Lh-1 kg- 1). The ratio of renal clearance to GFR is 2.5 and 3.2 in humans and rats, respectively. Therefore, for famotidine, there is reasonably good agreement between renal clearance in humans and that expected from animal models. Extending this approach to the data for the six compounds published by Sawada64 tends to support this observation. Comparing the unbound renal clearance to GFR in the rat and human gave comparable ratios for cefoperazone (0.5 and 0.9), moxalactam (1.7 and 1.8), and cefazolin (7.7 and 3.6). For cefmetazole, the mechanism of renal excretion was similar in rat and human but the net secretion was much more pronounced in human (ratios of 1.6 in rat and 6 in human). For cefpiramide the ratios in rat (1.1) and human (0.7) were similar but the values indicated different excretion processes, namely a net secretion in rat and a net reabsorption in human.

Nevertheless, important species differences might arise when active transport is involved in the renal disposition of compounds. For example, Giacomini and colleagues65 have shown that the human organic cation transporter (hOCT1) system is functionally different from the OCT1 homolog in other mammals, and it is reasonable to assume that these differences will modify their interactions with drugs. In this context, Dresser and co-workers66 determined the kinetics and substrate selectivity of the OCT1 homologs from mouse, rat, rabbit, and human, and found that the human homolog is functionally different from that of the rodent and rabbit.

Species differences in renal clearance might also occur through differences in urinary pH.67 Such effects might influence not only the extent of diffusion and reabsorption across the renal tubule, but could also result in artificial differences due to changes in the stability at different urinary pHs.67 Interspecies differences in biliary clearance

In general, mice, rats, and dogs are good biliary excretors, while guinea pigs, monkeys, and humans are relatively poor biliary excretors. The species differences in biliary excretion become less marked when the molecular size of the drug being excreted exceeds 700 Da.53 Species variation in the efficiency of the transport systems is the more probable cause. However, as discussed earlier, differences in biliary anatomy (i.e., presence or absence of a gallbladder) can give rise to species differences in pharmacokinetics.53

The role of active transport processes and their impact on species differences in drug disposition have been reviewed recently.68 Species differences in active transport may result from differences in the nature, levels, and/or regulation of the various transport proteins, as well as from their sequence substrate/inhibitor specificities. Where data are available on rat and human orthologs, sequence identity is reasonably high (70-80%) for the OATP, OCT, and MDR72 transporter families. There is some evidence to suggest that the rate of transport in rat is greater than in humans. For example, some basic compounds were shown to be taken up ~ 10-fold faster in rat than human hepatocytes73. Also Km and Ki for a range of bases were significantly lower for rat OCT1 than human OCT1,70'71 suggesting a greater substrate affinity in rat. Species differences in the transport across the bile canalicular membrane of certain organic anions (temocaprilat, 2,4-dinitrophenyl-S-gluthatione and taurocholate, which are specific substrates of MRP2 and BSEP transporters) were shown to be due mainly to differences in Vmax rather than Km.74

5.03.7 Prediction to Humans

Preclinical in vivo studies are helpful to guide the design of the first clinical studies. A number of approaches including empirical methods and physiologically-based models are being used to predict human pharmacokinetics based on preclinical data (see 5.37 Physiologically-Based Models to Predict Human Pharmacokinetic Parameters). Among the empirical methods, allometric scaling explores the mathematical relationships between pharmacokinetic parameters from various animal species in order to make predictions in humans. Such methods are relatively easy to apply but place demands on resources for the collection of in vivo data in animals. Nevertheless, their application has led to useful predictions of individual pharmacokinetic parameters (e.g., clearance, fraction absorbed, volume of distribution). In general, however, such methods can only take account of 'passive' transport differences between species. Allometric scaling is based on a power function of the form:

where y is the dependent variable (e.g., clearance, volume of distribution, half-life), W (body weight) is the independent variable, and a and x are the allometric coefficient and exponent, respectively. The values of a and x can be estimated by linear least squares regression of the log transformed allometric equations, i.e., log(clearance, volume of distribution, half-life, etc.) = log(a) + x ■ log(W) [7]

The sign and magnitude of the exponent indicate how the physiological or pharmacokinetic variable is changing as a function of W75-77: for b<0, y decreases as Wincreases; for 0<b<1, y increases as the species get larger, but does not increase as rapidly as W when b = 1, y increases in direct proportion to the increases in W and when b> 1, y increases faster than W.

The allometric equations for the pharmacokinetic parameters tend to be of the same order of magnitude as those for the corresponding physiologic variables, i.e.:

3. volume of distribution (mL)~aW0 8 to aW10.

The allometric approach can be particularly useful for compounds that are primarily eliminated by physical transport processes and when distribution occurs through passive processes. In such cases, when their values depend upon underlying physical or physiological processes that scale according to body weight, parameters such as clearance and volume of distribution do lend themselves well to allometric scaling. However, it may be less reliable for compounds that exhibit marked species differences in distribution, metabolism, or excretion mechanisms and a number of authors have cautioned against its use for selecting doses for the first phase of human studies.78,79

Physiologically-based models (PBPK) can be used to explore, and help to explain, the mechanisms that lie behind species differences in pharmacokinetics and drug metabolism. Physiologically-based models are discussed in more details in Chapter 5.37. Such models can provide a rational basis for interspecies scaling of individual parameters, which can then be integrated to provide quantitative and time-dependent estimates of both the plasma and tissue concentrations in humans. Furthermore, being mechanistically based they can be used diagnostically to generate information on new compounds and to understand the sensitivity of the in vivo profile to compound properties. Recent developments of in silico and in vitro models, which can provide estimates of the input parameters for PBPK models, have dramatically reduced the amount of experimental work required to make use of them.


1. Lin, J.; Sahakian, D. C.; de Morais, S. M.; Xu, J. J.; Polzer, R. J.; Winter, S. M. Curr. Top. Med. Chem. 2003, 3, 1125-1154.

2. Garattini, S. Exp. Toxic. Pathol. 1996, 48, 142-151.

4. White, R. E.; Manitpisitkul, P DrugMetab. Dispos. 2001, 29, 391-400.

5. Allen, M. C.; Shah, T S.; Day, W W Pharm. Res. 1998, 15, 93-97.

6. Bayliss, M. K.; Frick, L. W Curr. Opin. Drug Disc. Devel. 1999, 2, 20-25.

7. Frick, L. W; Adkison, K. L.; Wells-Knecht, K. J.; Woolard, P; Higton, D. M. Pharmaceut. Sci. Technol. Today 1998, 1, 12-18.

9. Eddershaw, P J.; Beresford, A. P; Bayliss, M. K. Drug Disc. Today 2000, S, 409-414.

10. Marathe, P. H.; Shyu, W C.; Humphreys, W G. Curr. Pharm. Des. 2004, 10, 2991-3008.

11. Garner, R. C. Curr. DrugMetab. 2000, 1, 205-213.

12. Combes, R. D.; Berridge, T; Connelly, J.; Eve, M. D.; Garner, R. C.; Toon, S.; Wilcox, P Eur. J. Pharm. Sci. 2003, 19, 1-11.

14. Campbell, D. B. Eur. J. DrugMetab. Pharmacokinet. 1994, 19, 283-293.

15. Baillie, T A.; Cayen, M. N.; Fouda, H.; Gerson, R. J.; Green, J. D.; Grossman, S. J.; Klunk, L. J.; LeBlanc, B.; Perkins, D. G.; Shipley, L. A. Toxicol. Appl. Pharmacol. 2002, 182, 188-196.

16. Caldwell, J.; Gardner, I.; Swales, N. Toxicol. Pathol. 1995, 23, 102-114.

17. de Jong, W H.; Timmerman, A.; van Raaij, M. T Lab. Anim. 2001, 3S, 243-248.

18. Martin, L. F; Vary, T C.; Davis, P. K.; Munger, B. L.; Lynch, J. C.; Spangler, S.; Remick, D. G. Arch. Surg. 1991, 126, 1087-1093.

19. Evans, D. C.; Watt, A. P.; Nicoll-Griffith, D. A.; Baillie, T. A. Chem. Res. Toxicol. 2004, 17, 3-16.

20. Uetrecht, J. P Chem. Res. Toxicol. 1999, 12, 387-395.

21. Tucker, G. T.; Houston, J. B.; Huang, S. M. Eur. J. Pharm. Sci. 2001, 13, 417-428.

22. Zhou, H.; Tong, Z.; McLeod, J. F J. Clin. Pharmacol. 2004, 44, 120-134.

23. Bjornsson, T D.; Callaghan, J. T; Einolf, H. J.; Fischer, V; Gan, L.; Grimm, S.; Kao, J.; King, S. P.; Miwa, G.; Ni, L. et al. J. Clin. Pharmacol. 2003, 43, 443-469.

24. Rubas, W; Cromwell, M. E.; Shahrokh, Z.; Villagran, J.; Nguyen, T N.; Wellton, M.; Nguyen, T H.; Mrsny, R. J. J. Pharm. Sci. 1996, 85, 165-169.

25. Artursson, P; Artursson, P Crit. Rev. Ther. Drug Carrier Syst. 1991, 8, 305-330.

26. Artursson, P; Karlsson, J. Biochem. Biophys. Res. Commun. 1991, 175, 880-885.

27. Yamashita, S.; Tanaka, Y; Endoh, Y; Taki, Y; Sakane, T; Nadai, T; Sezaki, H. Pharm. Res. 1997, 14, 486-491.

28. Salphati, L.; Childers, K.; Pan, L.; Tsutsui, K.; Takahashi, L. J. Pharm. Pharmacol 2001, S3, 1007-1013.

29. Ungell, A. L.; Nylander, S.; Bergstrand, S.; Sjöberg, A.; Lennernas, H. J. Pharm. Sci. 1998, 87, 360-366.

30. De Boer, A. G.; de Lange, E. C. M.; van der Sandt, I. C. J.; Breimer, D. In The Blood-Brain Barrier and Drug Delivery to the CNS; Begley, D. J., Bradbury, M. W, Kreuter, J., Eds.; Culinary and Hospitality Industry Publications Services: London, UK, 2000, pp 77-92.

33. Jezequel, S. G. In Progress in Drug Metabolism; Gibson, G. G., Ed.; Taylor & Francis: London, UK, 1992; Vol. 13, pp 141-178.

35. Chiou, W. L.; Buehler, P. W. Pharm. Res. 2002, 19, 868-874.

36. Chiou, W L.; Jeong, H. Y; Chung, S. M.; Wu, T C. Pharm. Res. 2000, 17, 135-140.

37. Chiou, W L.; Ma, C.; Chung, S. M.; Wu, T C.; Jeong, H. Y Int. J. Clin. Pharmacol. Ther. 2000, 38, 532-539.

38. Chiou, W L.; Barve, A. Pharm. Res. 1998, 15, 1792-1795.

39. DeSesso, J. M.; Jacobson, C. F Food Chem. Toxicol. 2001, 39, 209-228.

40. Kararli, T T Biopharm. Drug Dispos. 1995, 16, 351-380.

41. Martinez, M.; Amidon, G.; Clarke, L.; Jones, W W; Mitra, A.; Riviere, J. Adv. Drug Deliv. Rev. 2002, 54, 825-850.

43. Carlsson, A.; Kostewicz, E.; Hanisch, G.; Krumk, K.; Nilsson, R.; Abrahamsson, B. Proceedings of the AAPS Pharmaceutica, Toronto, Canada, 2002.

44. He, Y L.; Murby, S.; Warhurst, G.; Gifford, L.; Walker, D.; Ayrton, J.; Eastmond, R.; Rowland, M. J. Pharm. Sci. 1998, 87, 626-633.

45. Rowland, M.; Tozer, T. N. Clinical Pharmacokinetics: Concept and Applications, 3rd ed.; Williams & Wilkins Media, London, 1995.

46. Rodgers, T.; Leahy, D.; Rowland, M. J. Pharm. Sci. 2005, 94, 1237-1248.

47. Rodgers, T.; Leahy, D.; Rowland, M. J. Pharm. Sci. 2005, 94, 1259-1276.

48. Sawada, Y; Hanano, M.; Sugiyama, Y; Harashima, H.; Iga, T. J. Pharmacokinet. Biopharm. 1984, 12, 587-596.

49. Fichtl, B.; Nieciecki, A.; Walter, K. In Advances in Drug Research; Mordenti, C. A., Ed. Academic Press: London, 1991, pp 117-177.

50. Poulin, P.; Theil, F P. J. Pharm. Sci. 2000, 89, 16-35.

51. Theil, F P.; Guentert, T W; Haddad, S.; Poulin, P Toxicol. Lett 2003, 138, 29-49.

52. Lave, T; Dupin, S.; Schmitt, C.; Chou, R. C.; Jaeck, D.; Coassolo, P. J. Pharm. Sci. 1997, 86, 584-590.

53. McNamara, P J. In ; Welling, P G., Tse, F L. S., Dighe, S. V, Eds.; Marcel Dekker: New York, 1991; Vol. 48, pp 267-300.

54. de Waziers, I.; Bouguet, J.; Beaune, P. H.; Gonzalez, F J.; Ketterer, B.; Barouki, R. Pharmacogenetics 1992, 2, 12-18.

55. Guengerich, F P Environ. Health Perspect. 1995, 103, 25-28.

56. Anzenbacher, P; Anzenbacherova, E. Nova Acta Leopoldina 2003, 329, 11-20.

57. Funae, Y; Imaoka, S. In Cytochrome P450; Greim, H., Ed.; Springer Verlag: Berlin, Germany, 1993; Vol. 105, pp 221-238.

58. Ubeaud, G.; Schmitt, C.; Jaeck, D.; Lave, T; Coassolo, P. Xenobiotica 1995, 25, 1381-1390.

59. Zuegge, J.; Schneider, G.; Coassolo, P; Lave, T Clin. Pharmacokinet. 2001, 40, 553-563.

60. Ito, K.; Houston, J. B. Pharm. Res. 2005, 22, 103-112.

62. Lin, J. H. Ernst Schering Res. Found Workshop 2002, 37, 33-47.

63. Lin, J. H. DrugMetab. Dispos. 1995, 23, 1008-1021.

64. Sawada, Y; Hanano, M.; Sugiyama, Y; Iga, T. J. Pharmacokinet. Biopharm. 1984, 12, 241-261.

65. Giacomini, K. M. J. Pharmacokinet. Biopharm. 1997, 25, 731-741.

66. Dresser, M. J.; Gray, A. T; Giacomini, K. M. J. Pharmacol. Exp. Ther. 2000, 292, 1146-1152.

67. Bonate, P. L.; Reith, K.; Weir, S. Clin. Pharmacokinet. 1998, 34, 375-404.

68. Ayrton, A.; Morgan, P Xenobiotica 2001, 31, 469-497.

69. Hsiang, B.; Zhu, Y; Wang, Z.; Wu, Y; Sasseville, V; Yang, W P.; Kirchgessner, TG. J. Biol. Chem. 1999, 274, 37161-37168.

70. Zhang, L.; Brett, C. M.; Giacomini, K. M. Annu. Rev. Pharmacol. Toxicol. 1998, 38, 431-460.

71. Zhang, L.; Schaner, M. E.; Giacomini, K. M. J. Pharmacol. Exp. Ther. 1998, 286, 354-361.

72. Klein, I.; Sarkadi, B.; Varadi, A. Biochim. Biophys. Acta 1999, 1461, 237-262.

73. Sandker, G. W; Weert, B.; Olinga, P.; Wolters, H.; Slooff, M. J.; Meijer, D. K.; Groothuis, G. M. Biochem. Pharmacol. 1994, 47, 2193-2200.

74. Ishizuka, H.; Konno, K.; Shiina, T; Naganuma, H.; Nishimura, K.; Ito, K.; Suzuki, H.; Sugiyama, Y J. Pharmacol. Exp. Ther. 1999, 290, 1324-1330.

75. Mordenti, J. Antimicrob. Agents Chemother. 1985, 27, 887-891.

76. Mordenti, J. J. Pharm. Sci. 1986, 75, 1028-1040.

77. Mordenti, J.; Chen, S. A.; Moore, J. A.; Ferraiolo, B. L.; Green, J. D. Pharm. Res. 1991, 8, 1351-1359.

78. Bonate, P. L.; Howard, D. J. Clin. Pharm. 2000, 40, 665-670.

79. Boxenbaum, H.; Dilea, C. J. Clin. Pharmacol. 1995, 35, 957-966.

80. Grass, G. M.; Sinko, P. J. Adv. Drug Deliv. Rev. 2002, 54, 433-451.


Thierry Lavé received his PharmD and PhD in Pharmaceutical Sciences from the University of Strasbourg in 1992 and 1993. He also received a statistical degree from the University of Paris in 1992 and did an internship in Hospital Pharmacy from 1988 to 1992 in Strasbourg. He joined Roche's Department of Preclinical Pharmacokinetics and Drug Metabolism in 1992 where he served for 2 years as a postdoctoral fellow. Thierry Lavé is currently a Scientific Expert at F Hoffmann-La Roche and took responsibility for the global modeling and simulation group in preclinical research in 2002. His main area of research is in preclinical modeling of safety, pharmacokinetics and formulation, physiologically based pharmacokinetics, interspecies scaling, drugdrug interactions, and all aspects related to early predictions of pharmacokinetics/pharmacodynamics in man during drug discovery and early drug development. He has published a number of papers in these areas and also teaches pharmacokinetics at the Universities of Basel, Strasbourg, Helsinki, and Besancon.

Christoph Funk studied Biology at the Swiss Federal Institute of Technology (ETH) in Zurich, Switzerland in the early 1980s. Following graduation in 1985, he then completed his PhD thesis at the same University on the metabolism of several natural products in plants and plant tissue cultures. Dr Funk continued these studies between 1990 and 1993 as a postdoctoral fellow at the Institute of Biological Chemistry at Washington State University in Pullman, USA). His research focused on the metabolism of several mono- and diterpenoids in plants, including the characterization of several cytochrome P450 monooxygenases and other metabolic enzymes involved in secondary product formation.

Dr Funk returned to Switzerland where he took up a post-doctoral post at the Botanical Institute of the University of Basel. In 1994 he became Laboratory Head of Drug Metabolism in the Nonclinical Drug Development unit of F Hoffmann-La Roche in Basel. Besides the metabolic characterization of many new drug molecules, the research focused on the setup of in vitro drug transport assays and their characterization. The goal is to understand the relevant enzymatic steps involved in drug distribution and elimination. Since 2000, Dr Funk has been responsible for the drug metabolism and pharmacokinetics (DMPK) technologies group, bringing together the disciplines of drug metabolism, drug transport, kinetics, and mechanistic toxicology, with the aim of better characterization of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of potential new drugs.

© 2007 Elsevier Ltd. All Rights Reserved Comprehensive Medicinal Chemistry II

No part of this publication may be reproduced, stored in any retrieval system or transmitted ISBN (set): 0-08-044513-6 in any form by any means electronic, electrostatic, magnetic tape, mechanical, photocopying, recording or otherwise, without permission in writing from the publishers ISBN (Volume 5) 0-08-044518-7; pp. 31-50

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